Recent advancements in the Neural Radiance Field (NeRF) have bolstered its
capabilities for novel view synthesis, yet its reliance on dense multi-view
training images poses a practical challenge. Addressing this, we propose
HourglassNeRF, an effective regularization-based approach with a novel
hourglass casting strategy. Our proposed hourglass is conceptualized as a
bundle of additional rays within the area between the original input ray and
its corresponding reflection ray, by featurizing the conical frustum via
Integrated Positional Encoding (IPE). This design expands the coverage of
unseen views and enables an adaptive high-frequency regularization based on
target pixel photo-consistency. Furthermore, we propose luminance consistency
regularization based on the Lambertian assumption, which is known to be
effective for training a set of augmented rays under the few-shot setting.
Leveraging the inherent property of a Lambertian surface, which retains
consistent luminance irrespective of the viewing angle, we assume our proposed
hourglass as a collection of flipped diffuse reflection rays and enhance the
luminance consistency between the original input ray and its corresponding
hourglass, resulting in more physically grounded training framework and
performance improvement. Our HourglassNeRF outperforms its baseline and
achieves competitive results on multiple benchmarks with sharply rendered fine
details. The code will be available.

通过集成位置编码 (IPE) 的锥体梯度，提出了一种在原始输入光线和其对应反射光线之间增加一束额外光线的 hourglass 策略来提高神经辐射场 (NeRF) 在新颖视角合成方面的能力，并基于亮度一致性的正则化思想，在几乎没有训练数据情况下训练一组增强光线，以实现更好的物理建模和性能。

HourglassNeRF：将沙漏塑造为束缚射线的一种少样本神经渲染方法

HourglassNeRF: Casting an Hourglass as a Bundle of Rays for Few-shot  Neural Rendering

Continual learning in real-world scenarios is a major challenge. A general
continual learning model should have a constant memory size and no predefined
task boundaries, as is the case in semi-supervised Video Object Segmentation
(VOS), where continual learning challenges particularly present themselves in
working on long video sequences. In this article, we first formulate the
problem of semi-supervised VOS, specifically online VOS, as a continual
learning problem, and then secondly provide a public VOS dataset, CLVOS23,
focusing on continual learning. Finally, we propose and implement a
regularization-based continual learning approach on LWL, an existing online VOS
baseline, to demonstrate the efficacy of continual learning when applied to
online VOS and to establish a CLVOS23 baseline. We apply the proposed baseline
to the Long Videos dataset as well as to two short video VOS datasets, DAVIS16
and DAVIS17. To the best of our knowledge, this is the first time that VOS has
been defined and addressed as a continual learning problem.

本文提出了一个基于正则化的持续学习方法用于解决在线视频对象分割问题（VOS），取得了很好的效果，同时建立了一个基准公共数据集 CLVOS23。这是首次将 VOS 作为一个持续学习问题来定义和解决。